It is well-established knowledge that customer engagement, regardless of industry, is a vital element that separates great companies from the rest. In the residential energy sector, one proven way to engage consumers is through energy disaggregation. In energy disaggregation, consumer's total energy consumption is analysed and attributed to different appliances in home so that consumer can take an informed decision about energy consumption.
There are instances where data limitations may cause consumers to have or receive disaggregation for only a portion of their consumption. Unfortunately, in such scenarios, the opportunity to educate the consumers on appliances that are not disaggregated may be lost.
In some cases, energy disaggregation of appliances may provide a partial itemization, often with limited coverage in terms of percentage of total energy consumption disaggregated. Moreover, many disaggregation techniques are limited to smart meter data only. For example, legacy non-smart meters with one reading per billing cycle may have limited data resolution to extract much meaningful appliance patterns using existing disaggregation techniques.
Some existing statistical models may attempt to use low-resolution data to output an itemization bases such determinations on regional research, such as surveys or questionnaire, and are not generally accurate. Some such models are known to take user feedback (e.g., “I don't have AC”) and readjust the itemization. This approach is agnostic to the user's actual consumption, and all users who have given the same feedback will have the same percentage breakdown. In other words, this approach does not provide a true item level disaggregation based on low-resolution data.
Some existing systems that attempt to utilize a high-resolution disaggregation models may attempt detect as many appliances as possible, and aggregate the rest into an “Other” category. This approach will suffer, as the “Other” category is often quite large as a percentage of whole house energy consumption.
Accordingly, disaggregation techniques and systems that may utilize both low-resolution data and high resolution data (such as, but not limited to data received from a smart meter) is desirable.